157,217 research outputs found

    Emptying and Refilling of Slime Glands in Atlantic (\u3cem\u3eMyxine glutinosa\u3c/em\u3e) and Pacific (\u3cem\u3eEptatretus stoutii\u3c/em\u3e) Hagfishes

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    Hagfishes are known for their unique defensive slime, which they use toward off gill-breathing predators. Although much is known about the slime cells (gland thread cells and gland mucous cells), little is known about how long slime gland refilling takes, or how slime composition changes with refilling or repeated stimulation of the same gland. Slime glands can be individually electrostimulated to release slime, and this technique was used to measure slime gland refilling times for Atlantic and Pacific hagfish. The amount of exudate produced, the composition of the exudate and the morphometrics of slime cells were analyzed during refilling, and as a function of stimulation number when full glands were stimulated in rapid succession. Complete refilling of slime glands for both species took 3–4 weeks, with Pacific hagfish achieving faster absolute rates of exudate recovery than Atlantic hagfish. We found significant changes in the composition of the exudate and in the morphometrics of slime cells from Pacific hagfish during refilling. Over successive stimulations of full Pacific hagfish glands, multiple boluses of exudate were released, with exudate composition, but not thread cell morphometrics, changing significantly. Finally, histological examination of slime glands revealed slime cells retained in glands after exhaustion. Discrepancies in the volume of cells released suggest that mechanisms other than contraction of the gland musculature alone may be involved in exudate ejection. Our results provide a first look at the process and timing of slime gland refilling in hagfishes, and raise new questions about how refilling is achieved at the cellular level

    Multi-Thread Hydrodynamic Modeling of a Solar Flare

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    Past hydrodynamic simulations have been able to reproduce the high temperatures and densities characteristic of solar flares. These simulations, however, have not been able to account for the slow decay of the observed flare emission or the absence of blueshifts in high spectral resolution line profiles. Recent work has suggested that modeling a flare as an sequence of independently heated threads instead of as a single loop may resolve the discrepancies between the simulations and observations. In this paper we present a method for computing multi-thread, time-dependent hydrodynamic simulations of solar flares and apply it to observations of the Masuda flare of 1992 January 13. We show that it is possible to reproduce the temporal evolution of high temperature thermal flare plasma observed with the instruments on the \textit{GOES} and \textit{Yohkoh} satellites. The results from these simulations suggest that the heating time-scale for a individual thread is on the order of 200 s. Significantly shorter heating time scales (20 s) lead to very high temperatures and are inconsistent with the emission observed by \textit{Yohkoh}.Comment: Submitted to Ap

    How quickly can we sample a uniform domino tiling of the 2L x 2L square via Glauber dynamics?

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    TThe prototypical problem we study here is the following. Given a 2L×2L2L\times 2L square, there are approximately exp⁥(4KL2/π)\exp(4KL^2/\pi ) ways to tile it with dominos, i.e. with horizontal or vertical 2×12\times 1 rectangles, where K≈0.916K\approx 0.916 is Catalan's constant [Kasteleyn '61, Temperley-Fisher '61]. A conceptually simple (even if computationally not the most efficient) way of sampling uniformly one among so many tilings is to introduce a Markov Chain algorithm (Glauber dynamics) where, with rate 11, two adjacent horizontal dominos are flipped to vertical dominos, or vice-versa. The unique invariant measure is the uniform one and a classical question [Wilson 2004,Luby-Randall-Sinclair 2001] is to estimate the time TmixT_{mix} it takes to approach equilibrium (i.e. the running time of the algorithm). In [Luby-Randall-Sinclair 2001, Randall-Tetali 2000], fast mixin was proven: Tmix=O(LC)T_{mix}=O(L^C) for some finite CC. Here, we go much beyond and show that cL2≀Tmix≀L2+o(1)c L^2\le T_{mix}\le L^{2+o(1)}. Our result applies to rather general domain shapes (not just the 2L×2L2L\times 2L square), provided that the typical height function associated to the tiling is macroscopically planar in the large LL limit, under the uniform measure (this is the case for instance for the Temperley-type boundary conditions considered in [Kenyon 2000]). Also, our method extends to some other types of tilings of the plane, for instance the tilings associated to dimer coverings of the hexagon or square-hexagon lattices.Comment: to appear on PTRF; 42 pages, 9 figures; v2: typos corrected, references adde

    Evolution of Conversations in the Age of Email Overload

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    Email is a ubiquitous communications tool in the workplace and plays an important role in social interactions. Previous studies of email were largely based on surveys and limited to relatively small populations of email users within organizations. In this paper, we report results of a large-scale study of more than 2 million users exchanging 16 billion emails over several months. We quantitatively characterize the replying behavior in conversations within pairs of users. In particular, we study the time it takes the user to reply to a received message and the length of the reply sent. We consider a variety of factors that affect the reply time and length, such as the stage of the conversation, user demographics, and use of portable devices. In addition, we study how increasing load affects emailing behavior. We find that as users receive more email messages in a day, they reply to a smaller fraction of them, using shorter replies. However, their responsiveness remains intact, and they may even reply to emails faster. Finally, we predict the time to reply, length of reply, and whether the reply ends a conversation. We demonstrate considerable improvement over the baseline in all three prediction tasks, showing the significant role that the factors that we uncover play, in determining replying behavior. We rank these factors based on their predictive power. Our findings have important implications for understanding human behavior and designing better email management applications for tasks like ranking unread emails.Comment: 11 page, 24th International World Wide Web Conferenc
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